Imaging spectroscopy relies on associating each pixel in the image with a spectrum representing the intensity at each wavelength. As a result, imaging spectroscopy provides an information-rich representation of the scene which combines spatial and compositional information which can be used for a wide variety of applications spanning from remote sensing to food security and health.
For spectral image classification, each pixel spectra can be viewed as an input vector in a high dimensional space. This treatment opens up the possibility of representing a scene in terms of a number of spectral prototypes which correspond to naturally occurring materials such as wood, paint, etc. These materials can be extracted from the scene and are, in general, unknown a priori. Moreover, these material prototypes should be consistent across pixels sharing similar spectra.
FIG. 1 illustrates a transformation 100 of first and second pixel radiance spectra 110 and 120 respectively, into a spectral space 130. The first pixel radiance spectrum 110 is sampled at two wavelengths λ1 and λ2. This results in radiance values 111 and 112. The radiance values 111 and 112 of the first pixel are represented by a first sample 131 in the two-dimensional spectral space 130.
Similarly, the second pixel radiance spectrum 120 is sampled at the same two wavelengths λ1 and λ2 resulting in radiance values 121 and 122, which are represented by a second sample 132 in the spectral space 130. In this way, the radiance spectra of many pixels can be represented in the same spectral space 130.
by a second sample 132 in the spectral space 130. In this way, the radiance spectra of many pixels can be represented in the same spectral space 130.
It is noted that in most applications the radiance spectra are sampled at far more points, such as one hundred. In fact, the sample wavelengths may be the same as the wavelengths of the hyperspectral image data. As a result, the sample space 130 is high-dimensional—one dimension for each wavelength.
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